Quantification strategies for two-dimensional liquid chromatography datasets using regions of interest and multivariate curve resolution approaches.

In this work, three chemometrics-based approaches are compared for quantification purposes when using two-dimensional liquid chromatography (LC×LC-MS), taking as a study case the quantification of amino acids in commercial drug mixtures. Although the approaches have been already used for one-dimensional gas or liquid chromatography, the main novelty of this work is the demonstration of their applicability to LC×LC-MS datasets. Besides, steps such as peak alignment and modelling, commonly applied in this type of data analysis, are not required with the approaches proposed here. In a first step, regions of interest (ROI) strategy is used for the spectral compression of the LC×LC-MS datasets. Then the first strategy consists of building a calibration curve from the areas obtained in this ROI compression step. Alternatively, the ROI intensity matrices can be used as input for a second analysis step employing the multivariate curve resolution alternating least squares (MCR-ALS) method. The main benefit of MCR-ALS is the resolution of elution and spectral profiles for each of the analytes in the mixture, even in the case of strong coelutions and high signal overlapping. Classical MCR-ALS based calibration curve from the peak areas resolved only applying non-negativity constraints (second strategy) is compared to the results obtained when an area correlation constraint is imposed during the ALS optimization (third strategy). All in all, similar quantification results were achieved by the three approaches but, especially in prediction studies, the more accurate quantification is obtained when the calibration curve is built from the peak areas obtained with MCR-ALS when the area correlation constraint is imposed.

Comparison of Multivariate ANOVA-Based Approaches for the Determination of Relevant Variables in Experimentally Designed Metabolomic Studies.

The use of chemometric methods based on the analysis of variances (ANOVA) allows evaluation of the statistical significance of the experimental factors used in a study. However, classical multivariate ANOVA (MANOVA) has a number of requirements that make it impractical for dealing with metabolomics data. For this reason, in recent years, different options have appeared that overcome these limitations. In this work, we evaluate the performance of three of these multivariate ANOVA-based methods (ANOVA simultaneous component analysis-ASCA, regularized MANOVA-rMANOVA, and Group-wise ANOVA-simultaneous component analysis-GASCA) in the framework of metabolomics studies. Our main goals are to compare these various ANOVA-based approaches and evaluate their performance on experimentally designed metabolomic studies to find the significant factors and identify the most relevant variables (potential markers) from the obtained results. Two experimental data sets were generated employing liquid chromatography coupled to mass spectrometry (LC-MS) with different complexity in the design to evaluate the performance of the statistical approaches. Results show that the three considered ANOVA-based methods have a similar performance in detecting statistically significant factors. However, relevant variables pointed by GASCA seem to be more reliable as there is a strong similarity with those variables detected by the widely used partial least squares discriminant analysis (PLS-DA) method.

Understanding temporal and spatial changes of O3 or NO2 concentrations combining multivariate data analysis methods and air quality transport models.

The application of the multivariate curve resolution method to the analysis of temporal and spatial data variability of hourly measured O3 and NO2 concentrations at nineteen air quality monitoring stations across Catalonia, Spain, during 2015 is shown. Data analyzed included ground-based experimental measurements and predicted concentrations by the CALIOPE air quality modelling system at three horizontal resolutions (Europe at 12 × 12 km2, Iberian Peninsula at 4 × 4 km2 and Catalonia at 1 × 1 km2). Results obtained in the analysis of these different data sets allowed a better understanding of O3 and NO2 concentration changes as a sum of a small number of different contributions related to daily sunlight radiation, seasonal dynamics, traffic emission patterns, and local station environments (urban, suburban and rural). The evaluation of O3 and NO2 concentrations predicted by the CALIOPE system revealed some differences among data sets at different spatial resolutions. NO2 predictions, showed in general a better performance than O3 predictions for the three model resolutions, specially at urban stations. Our results confirmed that the application of the trilinearity constraint during the multivariate curve resolution factor analysis decomposition of the analyzed data sets is a useful tool to facilitate the understanding of the resolved variability sources.

Photostability study of multicomponent drug formulations via MCR-ALS: The case of the hydrochlorothiazide-amiloride mixture.

The kinetics and photodegradation mechanism of the pharmaceutical mixture of hydrochlorothiazide (HCT) and amiloride (AML) has been studied in depth using a chemometric approach. Water solutions of HCT and AML, separately or in binary mixtures, were irradiated with forced light at different pH values (3, 7, 9 and 12). Multivariate Curve Resolution - Alternating Least Squares (MCR-ALS) modelling has been applied to the experimental data recorded by UV spectrophotometry and HPLC-UV/MS. 78 data sets were collected and their chemometric processing has allowed the simultaneous determination of the behaviour of the two drugs in the mixture when exposed to light and the dependence of their photodegradation kinetics on pH. MCR-ALS has been applied using three different implementations. Soft-MCR-ALS and hybrid Hard/Soft-MCR-ALS have been used to resolve the experimental data and to get the equilibrium and kinetic parameters of the investigated chemical processes. A third implementation of the MCR-ALS method has been used in the analysis of the incomplete data sets obtained when UV spectrophotometric and HPLC-UV/MS data were simultaneously analysed, using a row- and column-wise incomplete augmented data matrix arrangement. In these matrices, information from HPLC-UV detector was used as a bridge between the data recorded by UV spectrophotometry (acid-base and kinetic reactions monitoring) and the data obtained by HPLC-MS.

Chemometric analysis for river water quality assessment at the intake of drinking water treatment plants.

Most of the Mediterranean rivers are suffering the effects of industrial, urban and mining discharges, as well as a reduction in water quantity and quality. Additionally, due to the Mediterranean climate, the natural water resource availability is periodically lower than the water demand in the area. Operation of drinking water plants in these geographical areas needs advanced process control systems where real-time and in-line water quality monitoring tools are key components. Data sets with parameters generated by monitoring sensors and from laboratory analysis are used to reveal the main factors that characterize water quality. Chemometric tools like Principal Component Analysis (PCA) can be used to explore and analyze correlations among different physicochemical and microbiological parameters with the aim to assess the river water quality at the water intake of drinking water treatment plants (DWTPs). Strong seasonal trends in the organic and inorganic matter contents and unusual events in the raw river water quality at the DWTP water intake are revealed. Organic and inorganic patterns are then associated with climatological, meteorological and industrial pollution circumstances typical for the geographical region under study. In addition, microbiological events can be detected at the water intake of DWTP which may occur simultaneously with increasing water contents of organic matter, especially at the beginning of rainfall episodes. The application of PCA on sensors data in the water intake at DWTPs offers new possibilities for improved quality assurance and control procedures for DWTP management and its strategy.

Predicting consumer preferences for mineral composition of bottled and tap water.

The overall liking for taste of water was correlated with the mineral composition of selected bottled and tap waters. Sixty-nine untrained volunteers assessed and rated twenty-five different commercial bottled and tap waters from. Water samples were physicochemical characterised by analysing conductivity, pH, total dissolved solids (TDS) and major anions and cations: HCO3-, SO42-, Cl-, NO3-, Ca2+, Mg2+, Na+, and K+. Residual chlorine levels were also analysed in the tap water samples. Globally, volunteers preferred waters rich in calcium bicarbonate and sulfate, rather than in sodium chloride. This study also demonstrated that it was possible to accurately predict the overall liking by a Partial Least Squares regression using either all measured physicochemical parameters or a reduced number of them. These results were in agreement with previously published results using trained panellists.

Influence of minerals on the taste of bottled and tap water: a chemometric approach.

Chemometric analysis was performed on two sets of sensory data obtained from two separate studies. Twenty commercially-available bottled mineral water samples (from the first study) and twenty-five drinking tap and bottled water samples (from the second study) were blind tasted by trained panelists. The panelists expressed their overall liking of the water samples by rating from 0 (worst flavor) to 10 (best flavor). The mean overall score was compared to the physicochemical properties of the samples. Thirteen different physicochemical parameters were considered in both studies and, additionally, residual chlorine levels were assessed in the second study. Principal component analysis performed on the physicochemical parameters and the panelists' mean scores generated models that explain most of the total data variance. Moreover, partial least squares regression of the panelists' sensory evaluations of the physicochemical data helped elucidate the main features underlying the panelists' ratings. The preferred bottled and tap water samples were associated with moderate (relatively to the parameters mean values) contents of total dissolved solids and with relatively high concentrations of HCO3⁻, SO4²â», Ca²âº and Mg²âº as well as with relatively high pH values. High concentrations of Na⁺, K⁺ and Cl⁻ were scored low by many of the panelists, while residual chlorine did not affect the ratings, but did enable the panel to distinguish between bottled mineral water and tap water samples.

Linear and non-linear chemometric modeling of THM formation in Barcelona's water treatment plant.

The complex behavior observed for the dependence of trihalomethane formation on forty one water treatment plant (WTP) operational variables is investigated by means of linear and non-linear regression methods, including kernel-partial least squares (K-PLS), and support vector machine regression (SVR). Lower prediction errors of total trihalomethane concentrations (lower than 14% for external validation samples) were obtained when these two methods were applied in comparison to when linear regression methods were applied. A new visualization technique revealed the complex nonlinear relationships among the operational variables and displayed the existing correlations between input variables and the kernel matrix on one side and the support vectors on the other side. Whereas some water treatment plant variables like river water TOC and chloride concentrations, and breakpoint chlorination were not considered to be significant due to the multi-collinear effect in straight linear regression modeling methods, they were now confirmed to be significant using K-PLS and SVR non-linear modeling regression methods, proving the better performance of these methods for the prediction of complex formation of trihalomethanes in water disinfection plants.

Factorial analysis of the trihalomethane formation in the reaction of colloidal, hydrophobic, and transphilic fractions of DOM with free chlorine.

BACKGROUND, AIM, AND SCOPE: This study focuses on the factors that affect trihalomethane (THMs) formation when dissolved organic matter (DOM) fractions (colloidal, hydrophobic, and transphilic fractions) in aqueous solutions were disinfected with chlorine. MATERIALS AND METHODS: DOM fractions were isolated and fractionated from filtered lake water and were characterized by elemental analysis. The investigation involved a screening Placket-Burman factorial analysis design of five factors (DOM concentration, chlorine dose, temperature, pH, and bromide concentration) and a Box-Behnken design for a detailed assessment of the three most important factor effects (DOM concentration, chlorine dose, and temperature). RESULTS: The results showed that colloidal fraction has a relatively low contribution to THM formation; transphilic fraction was responsible for about 50% of the chloroform generation, and the hydrophobic fraction was the most important to the brominated THM formation. DISCUSSION: When colloidal and hydrophobic fraction solutions were disinfected, the most significant factors were the following: higher DOM fraction concentration led to higher THM concentration, an increase of pH corresponded to higher concentration levels of chloroform and reduced bromoform, higher levels of chlorine dose and temperature produced a rise in the total THM formation, especially of the chlorinated THMs; higher bromide concentration generates higher concentrations of brominated THMs. Moreover, linear models were implemented and response surface plots were obtained for the four THM concentrations and their total sum in the disinfection solution as a function of the DOM concentration, chlorine dose, and temperature. Overall, results indicated that THM formation models were very complex due to individual factor effects and significant interactions among the factors. CONCLUSIONS: In order to reduce the concentration of THMs in drinking water, DOM concentrations must be reduced in the water prior to the disinfection. Fractionation of DOM, together with an elemental analysis of the fractions, is important issue in the revealing of the quality and quantity characteristics of DOM. Systematic study composed from DOM fraction investigation and factorial analysis of the responsible parameters in the THM formation reaction can, after an evaluation of the adjustment of the models with the reality, serves well for the evaluation of the spatial and temporal variability in the THM formation in dependence of DOM. However, taking into consideration the natural complexity of DOM, different operations and a strict control of them (like coagulation/flocculation and filtration) has to be used to quantitatively remove DOM from the raw water. RECOMMENDATIONS AND PERSPECTIVES: Assuming that this study represents a local case study, similar experiments can be easily applied and will supply with relevant information every local water treatment plant meeting problems with THM formation. The coagulation/flocculation and the filtration stages are the main mechanisms to remove DOM, particularly the colloidal DOM fraction. With the objective to minimize THMs generation, different unit operation designed to quantitatively remove DOM from water must be optimized.

Phytochemical analysis and in vitro cytotoxic activity of volatiles from Astragalus corniculatus.

The volatile fractions from Astragalus corniculatus Bieb., cultivated and collected wild, were analyzed at three different phenological phases for the first time. GC/MS analysis showed that the volatile fractions contain hydrocarbons, butyl ethers, acids, alcohols, esters, aldehydes, ketones, terpenes. These fractions were tested for cytotoxic activity in a panel of human tumor cell lines after 48 h, using the MTT-dye reduction assay. Throughout the cytotoxicity evaluation the fraction derived from the flowering phase of wild type plant was found to exert the most prominent cytotoxic activity, which could be ascribed to the high content of hydrocarbons and squalene in particular. Furthermore, the mechanistic elucidation of the mode of action of this volatile fraction in SKW-3 cells revealed that the observed activity is mediated by induction of necrotic type cell death as evidenced by the smear patterns of DNA following a 24 h exposure period.

Chemometric modeling and prediction of trihalomethane formation in Barcelona's water works plant.

Formation and occurrence of trihalomethanes (CHCl3, CHBr3, CHCl2Br, and CHBr2Cl) are investigated in water chlorination disinfection processes in the Barcelona's water works plant (WWP). Twenty-three WWP variables were measured and investigated for correlation with trihalomethane formation. Multivariate statistical methods including principal component analysis (PCA), multilinear regression (MLR), stepwise MLR (SWR), principal component regression (PCR) and partial least squares regression (PLSR) have been used and compared to model and predict the complex behavior observed for the measured trihalomethane concentrations. The results, obtained by PCA as well as the evaluation of the statistical significance of the coefficients in the linear regression vectors, revealed that the most important WWP variables for trihalomethane formation were: water temperature, total organic carbon, added chlorine concentrations, UV absorbance and turbidity at different sites of the WWP, as well as other variables like wells supply flow levels and carbon filters age. Overall, MLR and PLSR methods performed the best and gave similar good predictive properties. Best results were obtained for the total sum of trihalomethane concentrations, TTHM, with average modeling and prediction relative errors of 12% and 16%, respectively. Among the individual trihalomethanes, the concentrations of CHBr3 were the worst predicted ones with average modeling and prediction relative errors between 21-25% and 29-31%, respectively, followed by CHCl2Br with 23-26% and 25-27%. Better predictions were obtained for the concentrations of CHBr2Cl with relative modeling and prediction errors varying between 14-17% and 21%, and for the concentrations of CHCl3 with 21-24% and 23-25% errors, respectively.

Flavonoids from Astragalus hamosus.

A new flavonol glycoside 7-O-methyl-kaempferol 4'-beta-D-galactopyranoside (rhamnocitrin 4'-beta-D-galactopyranoside) (1) was isolated from the aerial parts of Astragalus hamosus. The known flavonols hyperoside (2), isoquercitrin (3) and astragalin (4) were also identified. Structures of the compounds were elucidated by chemical and spectral methods.

Volatiles from four Astragalus species: phenological changes and their chemotaxonomical application.

This paper shows the changes of the volatile compounds from four Astragalus species at three phenological stages: leaf development, flowering and fructification, which might be connected with the plant defense. After GC/MS analyses of Astragalus glycyphyllos L., A. hamosus L., A. cicer L. and A. spruneri Boiss., different groups of volatile compounds were found: hydrocarbons, alcohols, aldehydes and ketones, esters, terpenes, chlorinated compounds, etc. Identified volatiles were used for a cluster analysis in order to make chemotaxonomic conclusions for these evolutionary different species.